A market-based transmission planning for HVDC grid—case study of the North Sea

There is significant interest in building HVDC transmission to carry out transnational power exchange and deliver cheaper electricity from renewable energy sources which are located far from the load centers. This paper presents a market-based approach to solve a long-term TEP for meshed VSC-HVDC grids that connect regional markets. This is in general a nonlinear, non-convex large-scale optimization problem with high computational burden, partly due to the many combinations of wind and load that become possible. We developed a two-step iterative algorithm that first selects a subset of operating hours using a clustering technique, and then seeks to maximize the social welfare of all regions and minimize the investment capital of transmission infrastructure subject to technical and economic constraints. The outcome of the optimization is an optimal grid design with a topology and transmission capacities that results in congestion revenue paying off investment by the end the project's economic lifetime. Approximations are made to allow an analytical solution to the problem and demonstrate that an HVDC pricing mechanism can be consistent with an AC counterpart. The model is used to investigate development of the offshore grid in the North Sea. Simulation results are interpreted in economic terms and show the effectiveness of our proposed two-step approach

Analysis of North Sea offshore wind power variability

This paper evaluates, for a 2030 scenario, the impact on onshore power systems in terms of the variability of the power generated by 81 GW of offshore wind farms installed in the North Sea. Meso-scale reanalysis data are used as input for computing the hourly power production for offshore wind farms, and this total production is analyzed to identify the largest aggregated hourly power variations. Based on publicly available information, a simplified representation of the coastal power grid is built for the countries bordering the North Sea. Wind farms less than 60 km from shore are connected radially to the mainland, while the rest are connected to a hypothetical offshore HVDC (High-Voltage Direct Current) power grid, designed such that wind curtailment does not exceed 1% of production. Loads and conventional power plants by technology and associated cost curves are computed for the various national power systems, based on 2030 projections. Using the MATLAB-based MATPOWER toolbox, the hourly optimal power flow for this regional hybrid AC/DC grid is computed for high, low and medium years from the meso-scale database. The largest net load variations are evaluated per market area and related to the extra load-following reserves that may be needed from conventional generators.Parts of this work were funded by Agentschap.NL, the Netherlands, now RVO.nl (Rijksdienst voor Ondernemend Nederland [25], under the project North Sea Transnational Grid (NSTG). The NSTG project is a cooperation between Delft University of Technology and the Energy Research Center of the Netherlands

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

SCALE-UP OF THE MILENA GASIFICATION PROCESS FOR THE PRODUCTION OF BIO-SNG

The production of Substitute Natural Gas from biomass (Bio-SNG) is an attractive option to reduce CO2 emissions and replace declining fossil natural gas reserves. The Energy research Center of the Netherlands (ECN) is working on the development of a technology to convert a wide range of fuels into Bio-SNG. The ECN Bio-SNG technology is based on indirect gasification of biomass. The MILENA indirect gasifier is developed to produce a gas, which can be upgraded into SNG with a high efficiency. Because of the indirect heating of the gasification process no oxygen is required. Char and tar are removed form the producer gas and are used as fuel to produce the required heat for the gasification process. The OLGA tar removal technology is used to remove tar and dust from the gas. After gas cleaning the gas is catalytically converted into a mixture of CH4, CO2 and H2O. After compression and removal of CO2 and H2O, the remaining methane can be used as Bio-SNG. ECN produced the first Bio-SNG in 2004, using a conventional fluidized bed gasifier. The lab-scale MILENA gasifier was built in 2004. The installation is capable of producing approximately 8 mn3/h methane-rich medium calorific gas with high efficiency. The lab-scale installation has been in operation for more than 1000 hours now and is working fine. Several biomass fuels were tested. Woody biomass appears to be the most suited fuel. The lab-scale gasifier is coupled to lab-scale gas cleaning installations (including OLGA) and a methanation unit. The integrated system was tested several times. ECN has recently finished the construction of an 800 kWth pilot-scale gasifier which was taken into operation in the summer of 2008. First results, using wood as a fuel, show that the gas composition is similar to gas from the lab-scale installation. The pilot scale gasifier will be coupled to the existing pilot scale OLGA gas cleaning unit. ECN aims to demonstrate a 10 MWth SNG production plant, based on the ECN MILENA and OLGA technology in the near future. The scale foreseen for a commercial single-train Bio-SNG production facility is between 50 and 500 MWth. The expected net overall efficiency from wood to Bio-SNG is 70%. Visit www.BioSNG.com, www.Olgatechnology.com or www.Milenatechnology.com for more information. During the conference an overview will be given of the latest results from the lab-scale methanation experiments, the first results from the 800 kWth MILENA pilot plant and the plans for the near future

Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

The participation of volatile wind energy resources in the generation mix of power systems is increasing. It is therefore becoming more and more crucial for system operators to accurately predict the wind power generation across different short term horizons (5 to 60 minutes ahead) in order to adequately balance the system and maintain system security. This paper presents a comprehensive assessment of the influence of different parameters in artificial neural networks, such as the amount of historic data, batch size, number of hidden layers, number of neurons per hidden layer, and the amount of training data on the short term forecast accuracy. In order to identify the parameters which are most influential with respect to forecast accuracy, a sensitivity study isolating the various factors on a one-At-A-Time basis has been performed. To minimize the forecast error across the investigated forecast horizons, the developed neural networks use the feed forward back propagation algorithm. From the investigated cases it is concluded that a neural network with two hidden layers is most suitable for wind forecasting on the timeframes considered. Furthermore, with increasing forecast horizons (from 5 to 60 minutes ahead), better performance is achieved when neural networks contain increased neurons in the hidden layers and have enlarged training data sets.Intelligent Electrical Power Grid

Effecten van klimaatsverandering op planten in Nederland

Environmental Biology - ol

Effecten van klimaatsverandering op hogere planten in Nederland

Environmental Biology - ol

New Cycle-based Formulation, Cost Function, and Heuristics for DC OPF Based Controlled Islanding

This paper presents a new formulation for intentional controlled islanding (ICI) of power transmission grids based on mixed-integer linear programming (MILP) DC optimal power flow (OPF) model. We highlight several deficiencies of the most well-known formulation for this problem and propose new enhancements for their improvement. In particular, we propose a new alternative optimization objective that may be more suitable for ICI than the minimization of load shedding, a new set of island connectivity constraints, and a new set of constraints for DC OPF with switching, and a new MILP heuristic to find initial feasible solutions for ICI. It is shown that the proposed improvements help to reduce the final optimality gaps as compared to the benchmark model on several test instances.Comment: https://doi.org/10.1016/j.epsr.2022.10858

Forced expression of the interferon regulatory factor 2 oncoprotein causes polyploidy and cell death in FDC-P1 myeloid hematopoietic progenitor cells

The IFN regulatory factor-2 (IRF-2) oncoprotein controls the cell cycle-dependent expression of histone H4 genes during S phase and may function as a component of an E2F-independent mechanism to regulate cell growth. To investigate the role of IRF-2 in control of cell proliferation, we have constructed a stable FDC-P1 cell line (F2) in which expression of IRF-2 is doxycycline (DOX)-inducible, and a control cell line (F0). Both the F2 and F0 cell lines were synchronized in the G1 phase by isoleucine deprivation, and IRF-2 was induced by DOX on release of cells from the cell cycle block. Flow cytometric analyses indicated that forced expression of IRF-2 has limited effects on cell cycle progression before the first mitosis. However, continued cell growth in the presence of elevated IRF-2 levels results in polyploidy (\u3e4n) or genomic disintegration (\u3c2n) and cell death. Western blot analyses revealed that the levels of the cell cycle regulatory proteins cyclin B1 and the cyclin-dependent kinase (CDK)-inhibitory protein p27 are selectively increased. These changes occur concomitant with a significant elevation in the levels of the FAS-L protein, which is the ligand of the FAS (Apo1/CD95) receptor. We also found a subtle change in the ratio of the apoptosis-promoting Bax protein and the antiapoptotic Bcl-2 protein. Hence, IRF-2 induces a cell death response involving the Fas/FasL apoptotic pathway in FDC-P1 cells. Our data suggest that the IRF-2 oncoprotein regulates a critical cell cycle checkpoint that controls progression through G2 and mitosis in FDC-P1 hematopoietic progenitor cells